The present invention relates to an interference cancellation receiver and method. In particular, the present invention relates to an interference cancellation receiver architecture used to estimate coefficients of a linear interference cancellation filter.
In order to efficiently and cost-effectively provide services to a large number of cellular subscribers, it is important that networks are designed and deployed in a manner allowing spectral efficiency to be maximised. This can be achieved by employing frequency re-use techniques where the frequency spectrum is shared between multiple cells. However, the re-use of the same frequency resource leads to an increase in the level of interference generated by users active in the network. In today's heavily loaded cellular networks, capacity is therefore limited by interference rather than by thermal noise.
A number of different techniques have been considered in the past in order to reduce the performance degradation due to interference and increasing the network capacity. Adaptive Multi-Rate (AMR), dynamic power control, discontinuous transmission, dynamic channel allocation and frequency hopping are examples of such techniques. However, most of these techniques require significant upgrades to the network infrastructure.
The two main sources of interference in cellular networks are CCI (Co-Channel Interference) coming from users operating on the same frequency and ACI (Adjacent Channel Interference) that is produced by users transmitting on adjacent carriers. It is possible to mitigate the performance degradation caused by adjacent channel interference through the use of filtering techniques (WO2006/027603). On the other hand, CCI is significantly more difficult to mitigate as it occupies the same spectrum as the signal of interest. When multiple antennas are used at the receiver, it is possible to make use of the spatial diversity in order to mitigate the effect of the CCI (“Improved Spatial-Temporal Equalization for EDGE: A Fast Selective-Direction MMSE Timing Recovery Algorithm and Two-Stage Soft-Output Equalizer”, Jack H. Winters, Hanks Zeng, and Ye Li, IEEE Transactions on Communications, December 2001). However, the use of multiple receive antennas is usually not feasible for mobile communication user terminals as the associated cost is too high.
FIG. 1 presents the different processing functions that are typically found in a mobile communication system. The transmitter 101 passes information bits through a block adding error protection coding 102 and then through a digital modulation block 103 which generates a digital complex base-band signal. Conversion to analogue and modulation to the desired carrier frequency is then performed by the RF processing unit 104. As part of the modulation, known symbols may be added to assist with radio channel estimation in the receiver.
Once transmitted, the radio signal then passes through the radio channel 105 before reception at a receiver 106. This radio channel frequently gives rise to Inter-Symbol Interference (ISI) which must then be removed by the receiver to ensure correct reception. Before being processed by the receiver blocks, the signal also acquires both interference and noise. The interference arises from other users of the spectrum whilst the noise is thermal noise from the environment. Additional noise is then added as the signal passes through the Rx front end 107.
The receiver 106 converts the analogue radio signal to a digital base band signal in the Rx front-end 107. The signal is then passed through the demodulation block 108. This serves to estimate the transmitted coded-bits in the presence of the ISI, interference and noise added by the radio channel and the Rx front end. The signal is then decoded 109 to yield the final received information bits.
The demodulation block 108 aims to recover the transmitted information bits from the received base-band signal in the presence of noise of interference. Typically, co-channel and adjacent channel interference is simply modelled as noise. The presence of interference in the communication system leads to an increase in the noise level and therefore degrades the link-level performance.
Recently, a number of different techniques have been proposed to mitigate the impact of CCI on the demodulation process. Interference cancellation can be achieved using different approaches which be can divided into two main categories. In BIC (Blind Interference Cancellation) techniques, the received signal is processed such that the quality of the desired user information is maximised without explicitly estimating the interfering signals. Examples of receiver architectures using BIC are presented in “Single-Antenna Co-Channel Interference Cancellation for TDMA Cellular Radio Systems”, P. A. Hoeher, S. Badri-Hoeher, W. Xu and C. Krakowski, IEEE Transactions on Wireless Communications, Volume 5, Issue 6, June 2006.
JD (Joint Detection) techniques, on the other hand, try and jointly demodulate the signal of interest as well as the main interference signal (“Co-channel Interference Cancellation Receiver for TDMA Mobile Systems”, P. A. Ranta, A. Hottinen and Z. C. Honkassalo, Proc. IEEE ICC'95, June 1995). JD techniques are best suited to synchronised networks and in such network configuration offer larger potential capacity gains. However, these techniques require the estimation of the propagation channel for the signal of the interfering user to be cancelled. As a result of this, the performance of JD techniques is sensitive to the relative timing of the wanted and interfering signals and the achievable gains in unsynchronised networks is reduced.
The complexity associated with the implementation of JD techniques is also usually very high. This contrasts with BIC techniques which offer a lower implementation complexity and can provide capacity gains in both synchronised and unsynchronised networks. Moreover, since BIC algorithms do not rely on the knowledge of the interfering channel, they can adapt to different and varying interference conditions.